Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach.

TitleEstimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach.
Publication TypeJournal Article
Year of Publication2023
AuthorsJin, H, Crimmins, E, Langa, KM, Dey, AB, Lee, J
ISSN Number1423-0208
KeywordsAged, Aging, Dementia, Female, Humans, India, Male, Middle Aged, Prevalence, supervised machine learning

INTRODUCTION: Accurate estimation of dementia prevalence is essential for making effective public and social care policy to support individuals and families suffering from the disease. The purpose of this paper is to estimate the prevalence of dementia in India using a semi-supervised machine learning approach based on a large nationally representative sample.

METHODS: The sample of this study is adults 60 years or older in the wave 1 (2017-2019) of the Longitudinal Aging Study in India (LASI). A subsample in LASI received extensive cognitive assessment and clinical consensus ratings and therefore has diagnoses of dementia. A semi-supervised machine learning model was developed to predict the status of dementia for LASI participants without diagnoses. After obtaining the predictions, sampling weights and age standardization to the World Health Organization (WHO) standard population were applied to generate the estimate for prevalence of dementia in India.

RESULTS: The prevalence of dementia for those aged 60 years and older in India was 8.44% (95% CI: 7.89%-9.01%). The age-standardized prevalence was estimated to be 8.94% (95% CI: 8.36%-9.55%). The prevalence of dementia was greater for those who were older, were females, received no education, and lived in rural areas.

DISCUSSION: The prevalence of dementia in India may be higher than prior estimates derived from local studies. These prevalence estimates provide the information necessary for making long-term planning of public and social care policy. The semi-supervised machine learning approach adopted in this paper may also be useful for other large population aging studies that have a similar data structure.

Citation Key13076
PubMed ID36617419
PubMed Central IDPMC10038923
Grant ListR01 AG051125 / AG / NIA NIH HHS / United States
U01 AG064948 / AG / NIA NIH HHS / United States
R01 AG042778 / AG / NIA NIH HHS / United States
R01 AG030153 / AG / NIA NIH HHS / United States